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Dec 7, 2025
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feat(altair): implement line-basic #373

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175 changes: 22 additions & 153 deletions plots/altair/line/line-basic/default.py
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Original file line number Diff line number Diff line change
@@ -1,166 +1,35 @@
"""
line-basic: Basic Line Chart
line-basic: Basic Line Plot
Library: altair
"""

from typing import TYPE_CHECKING

import altair as alt
import pandas as pd


if TYPE_CHECKING:
from altair import Chart

# Style guide colors
PYTHON_BLUE = "#306998"


def create_plot(
data: pd.DataFrame,
x: str,
y: str,
title: str | None = None,
xlabel: str | None = None,
ylabel: str | None = None,
color: str = PYTHON_BLUE,
linewidth: float = 2,
marker: str | None = None,
marker_size: int = 60,
alpha: float = 1.0,
linestyle: str = "solid",
**kwargs,
) -> "Chart":
"""
Create a basic line chart showing trends over a continuous axis.

Args:
data: Input DataFrame with required columns
x: Column name for x-axis values (typically time or sequence)
y: Column name for y-axis values
title: Plot title (default: None)
xlabel: Custom x-axis label (default: uses column name)
ylabel: Custom y-axis label (default: uses column name)
color: Line color (default: Python Blue #306998)
linewidth: Width of the line (default: 2)
marker: Marker style - 'circle', 'square', 'diamond', etc. (default: None)
marker_size: Size of markers if enabled (default: 60)
alpha: Transparency level for the line (default: 1.0)
linestyle: Line style - 'solid', 'dashed', 'dotted' (default: 'solid')
**kwargs: Additional parameters for altair chart configuration

Returns:
Altair Chart object

Raises:
ValueError: If data is empty
KeyError: If required columns not found

Example:
>>> data = pd.DataFrame({
... 'month': [1, 2, 3, 4, 5, 6],
... 'sales': [100, 150, 130, 180, 200, 190]
... })
>>> chart = create_plot(data, x='month', y='sales')
"""
# Input validation
if data.empty:
raise ValueError("Data cannot be empty")

for col in [x, y]:
if col not in data.columns:
available = ", ".join(data.columns)
raise KeyError(f"Column '{col}' not found. Available columns: {available}")

# Sort data by x-axis to ensure proper line connections
plot_data = data.sort_values(by=x).copy()
# Data
data = pd.DataFrame({"time": [1, 2, 3, 4, 5, 6, 7], "value": [10, 15, 13, 18, 22, 19, 25]})

# Determine x encoding type based on data
x_dtype = plot_data[x].dtype
if pd.api.types.is_datetime64_any_dtype(x_dtype):
x_encoding = f"{x}:T"
else:
x_encoding = f"{x}:Q"

# Map linestyle to altair strokeDash
stroke_dash_map = {"solid": [], "dashed": [8, 4], "dotted": [2, 2]}
stroke_dash = stroke_dash_map.get(linestyle, [])

# Create the line chart
line = (
alt.Chart(plot_data)
.mark_line(color=color, strokeWidth=linewidth, opacity=alpha, strokeDash=stroke_dash)
.encode(
x=alt.X(
x_encoding,
title=xlabel or x,
axis=alt.Axis(labelAngle=0, labelLimit=200, labelFontSize=16, titleFontSize=20),
),
y=alt.Y(
f"{y}:Q",
title=ylabel or y,
scale=alt.Scale(zero=False),
axis=alt.Axis(labelFontSize=16, titleFontSize=20),
),
tooltip=[
alt.Tooltip(x_encoding, title=xlabel or x),
alt.Tooltip(f"{y}:Q", title=ylabel or y, format=".2f"),
],
)
)

# Add markers if specified
if marker:
points = (
alt.Chart(plot_data)
.mark_point(color=color, size=marker_size, filled=True, shape=marker, opacity=alpha)
.encode(
x=alt.X(x_encoding),
y=alt.Y(f"{y}:Q"),
tooltip=[
alt.Tooltip(x_encoding, title=xlabel or x),
alt.Tooltip(f"{y}:Q", title=ylabel or y, format=".2f"),
],
)
)
chart_base = line + points
else:
chart_base = line

# Apply properties and configuration
# Target: 4800 × 2700 px with scale_factor=3.0 -> 1600 × 900 base
title_config = alt.TitleParams(text=title, fontSize=20, anchor="middle") if title else None
chart = (
chart_base.properties(width=1600, height=900, title=title_config)
.configure_view(strokeWidth=0)
.configure_axis(grid=True, gridOpacity=0.3, gridDash=[3, 3], domainWidth=1, tickWidth=1)
# Create line chart
line = (
alt.Chart(data)
.mark_line(strokeWidth=2, color="#306998")
.encode(
x=alt.X("time:Q", title="Time", axis=alt.Axis(labelFontSize=16, titleFontSize=20)),
y=alt.Y("value:Q", title="Value", axis=alt.Axis(labelFontSize=16, titleFontSize=20)),
)
)

return chart
# Add points on the line for clarity
points = alt.Chart(data).mark_point(size=100, color="#306998", filled=True).encode(x="time:Q", y="value:Q")

# Combine and configure chart
chart = (
(line + points)
.properties(width=1600, height=900, title=alt.Title("Basic Line Plot", fontSize=20))
.configure_view(strokeWidth=0)
.configure_axis(grid=True, gridOpacity=0.3)
)

if __name__ == "__main__":
# Sample data for testing - monthly sales trend
sample_data = pd.DataFrame(
{
"Month": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
"Sales": [120, 150, 170, 165, 180, 220, 250, 240, 210, 190, 180, 200],
}
)

# Create plot with markers
chart = create_plot(
sample_data,
x="Month",
y="Sales",
title="Monthly Sales Trend (2024)",
xlabel="Month",
ylabel="Sales (thousands)",
linewidth=2.5,
marker="circle",
marker_size=80,
)

# Save as PNG - scale_factor=3.0 gives 4800 × 2700 px
chart.save("plot.png", scale_factor=3.0)
print("Plot saved to plot.png")
# Save as PNG (1600 × 900 at scale 3 = 4800 × 2700 px)
chart.save("plot.png", scale_factor=3.0)
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